The research proposed focuses on linear and nonlinear models for longitudinal data for clinical and laboratory studies and data monitoring methods for these models as applied to clinical trials. Previous research efforts have concentrated on developing flexible data monitoring procedures which could be used for binary, continuous, or survival type outcomes. However, many clinical trials now obtain repeated measures over time. Those results can often be analyzed by linear or nonlinear models for repeated measures. Methods for monitoring accumulating data in this setting will be developed. Further research will include investigating the impact of informative right censoring and nonignorable missing data. The proposed approach for this research is based on the generalized group sequential methods previously developed. Another major goal of this work is to increase the use and acceptance of recently developed powerful models for repeated measures data analysis by developing and testing a model selection methodology. Diagnostic information obtained during parameter estimation will be incorporated into the model selection procedure. Models considered will include linear and nonlinear mixed effects models with or without time series type conditional covariance matrix. A related goal is to identify classes of designs that produce data that can be adequately modeled using a simple model even when the underlying model is more complex. Finally, existing software which implements past research as well as that proposed will be put into user friendly, well documented packages for distribution to colleagues.